1. Data-assimilation in flood forecasting for the river Rhine between Andernach and Düsseldorf COR-JAN VERMEULEN

2. Introduction 238 recorded floods in Europe between 1975 and 2001

3. Introduction Flood events Deaths per events

4. Introduction Huge investments in flood prevention, flood early warning, flood mitigation measures and flood management FloodMan: Near real-time flood forecasting, warning and management

5. Introduction Data-assimilation of hydrological and hydraulic parameters for flood forecasting Independent of the computer models used Use of in-situ and satellite data Pilot: Rhine river, Germany

6. Data-assimilation Combining model estimates with measured data Including measure of uncertainty for estimates

7. Pilot Rhine river

8. Flood forecasting system Rainfall-runoff Model (HBV) Water Transport Model Hydraulic Model (Sobek) Data-assimilation

9. actual measurements Hydrological model Hydro-meteo database runoff prediction Data- assimilation Filtered water levels and flows Data- assimilation Filtered model parameters Hydraulic model Prediction of water levels and flows Data-assimilation

10. Hydrological model Weather forecast Forecast tributaries Hydraulic model Flood forecast Forecast

11. Flood forecasting system Data-assimilation hydrological model Sensitivity and uncertainty analysis –Adaptation soil moisture content –Adaptation upper zone All sub basins treated equally Use adaptation factors in forecasting

12. Flood forecasting system Data-assimilation hydraulic model Sensitivity and uncertainty analysis –Adaptation roughness main channel –Adaptation lateral discharges Desired accuracy Until calculated water levels at Bonn and Cologne “agree” with measurements Use adaptation factors in forecasting Data- assimilation

13. Results

14. Conclusions data-assimilation in-situ data Large calculation time (10 minutes for a day) Relatively small changes parameters indicating: –well calibrated hydraulic model –robust data-assimilation algorithm Forecast pattern remains similar Average accuracy around 5 cm in water levels

16. Role of satellite data Use of satellite data in deducing water levels Additional information is to be used in data- assimilation of hydraulic model Satellite ‘measurements’ are, compared to in- situ measurements: –less accurate, but –more detailed

17. Example satellite data

18. Possible role of satellite data No real flood maps based on EO-data available for Rhine river, Germany Synthetic flood maps, using hydraulic model and a digital terrain model Introducing inaccuracies (‘noise’) by modelling errors in: -geo referencing; and -classification

19. Error in geo referencing

20. Error in classification

21. Procedure

22. Conclusions using flood maps Results depend on quality of satellite data –high resolution –low noise Flood maps to water levels –Area’s instead of cross-sections –stretches long enough (5 – 10 km) –straight river sections –gentle slopes, no steeps banks Opportunity –comparison of flood extent calculated and satellite data.

23. Conclusions FloodMan The flood forecasting system is robust and ready to serve under operational conditions; In the pilot small improvement in the flood forecast accuracy; Forecast including measure of uncertainty: useful for decision making. Use of satellite data is promising, especially for river systems with few gauging stations –BUT high resolution satellite data needed

24. Further work Flood forecast systems with data-assimilation on hydrological and hydraulic model are implemented Different data-assimilation algorithms Data-assimilation to combine rainfall radar data with in-situ measurements Use of satellite data to determine flood extent in case of dike breach for: –estimate width and depth of dike breach –estimate discharge at dike breach